Emulating real-world GLP-1 efficacy in type 2 diabetes through causal learning and virtual patients.

Journal: PLOS digital health
Published Date: Jul 21, 2025

Abstract

Randomized controlled trials (RCTs) remain the benchmark for assessing treatment effects but are limited to phenotypically narrow populations by design. We introduce a novel generative artificial intelligence (AI) driven emulation method that infers effect size through virtual clinical trials, which can emulate the RCT process and potentially extrapolate into wider populations. We validate the virtual trials by comparing the predicted impact of glucagon-like peptide-1 (GLP-1) agonists on HbA1c in type-2 diabetes (T2DM) with its true efficacy established in the LEAD-5 trial. Our emulation model learns treatment effects from real-world evidence data by a combined generative AI and causal learning approach. Training data comprised pre- and post-treatment outcomes for 5,476 people with T2DM. We considered three treatment arms: GLP-1 (Liraglutide), basal insulin (glargine), and placebo. After training, virtual trials were conducted by sampling 232 virtual patients per arm (according to the LEAD-5 inclusion criteria) and predicting post-treatment outcomes. We used difference-in-differences (DiD) for pairwise comparisons between arms. Our goal was to emulate LEAD-5 by demonstrating a significant DiD in post-treatment HbA1c reduction for GLP-1 compared to basal insulin and placebo. We found significant differences in HbA1c reduction for GLP-1 vs basal insulin (-1.21 mmol/mol (-0.11%); p < 0.001) and GLP-1 vs placebo (-2.58 mmol/mol (-0.24%); p < 0.001) in our virtual populations, consistent with LEAD-5 (Liraglutide vs glargine: -2.62mmol/mol (-0.24%); p = 0.0015, Liraglutide vs placebo: -11.91 mmol/mol (-1.09%); p < 0.0001). The causal AI-powered clinical trials can emulate LEAD-5 in important measurements for T2DM. Our algorithm is specialty agnostic and can explore counterfactual questions, making it suitable for further study in the generalizability of RCT results in real-world populations to support clinical decision-making and policy recommendations.

Authors

  • Calum Robert MacLellan
    Department of Biomedical Engineering, University of Strathclyde, Glasgow, United Kingdom.
  • Hristo Petkov
    Department of Computer and Information Sciences, University of Strathclyde, Glasgow, United Kingdom.
  • Conor McKeag
    School of Cardiovascular & Metabolic Health, University of Glasgow NHS Greater Glasgow and Clyde, Glasgow, United Kingdom.
  • Feng Dong
    School of Economics and Management, China University of Mining and Technology, Xuzhou 221116, China.
  • David John Lowe
    Digital Health Validation Lab, University of Glasgow, Glasgow, United Kingdom.
  • Roma Maguire
    University of Strathclyde, Glasgow, Scotland.
  • Sotiris Moschoyiannis
    School of Computer Science and Electronic Engineering, University of Surrey, Guildford, United Kingdom.
  • Jo Armes
    Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom,.
  • Simon Skene
    Surrey Clinical Trials Unit, University of Surrey, Guildford, United Kingdom.
  • Alastair Finlinson
    School of Computer Science and Electronic Engineering, University of Surrey, Guildford, United Kingdom.
  • Christopher Sainsbury
    School of Cardiovascular & Metabolic Health, University of Glasgow NHS Greater Glasgow and Clyde, Glasgow, United Kingdom.

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